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Identifying and Analyzing Uncertainty Structures in the TRMM Microwave Imager Precipitation Product over Tropical Ocean BasinsDespite continuous improvements in microwave sensors and retrieval algorithms, our understanding of precipitation uncertainty is quite limited, due primarily to inconsistent findings in studies that compare satellite estimates to in situ observations over different parts of the world. This study seeks to characterize the temporal and spatial properties of uncertainty in the Tropical Rainfall Measuring Mission Microwave Imager surface rainfall product over tropical ocean basins. Two uncertainty analysis frameworks are introduced to qualitatively evaluate the properties of uncertainty under a hierarchy of spatiotemporal data resolutions. The first framework (i.e. 'climate method') demonstrates that, apart from random errors and regionally dependent biases, a large component of the overall precipitation uncertainty is manifested in cyclical patterns that are closely related to large-scale atmospheric modes of variability. By estimating the magnitudes of major uncertainty sources independently, the climate method is able to explain 45-88% of the monthly uncertainty variability. The percentage is largely resolution dependent (with the lowest percentage explained associated with a 1 deg x 1 deg spatial/1 month temporal resolution, and highest associated with a 3 deg x 3 deg spatial/3 month temporal resolution). The second framework (i.e. 'weather method') explains regional mean precipitation uncertainty as a summation of uncertainties associated with individual precipitation systems. By further assuming that self-similar recurring precipitation systems yield qualitatively comparable precipitation uncertainties, the weather method can consistently resolve about 50 % of the daily uncertainty variability, with only limited dependence on the regions of interest.
Document ID
20160014493
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Liu, Jianbo
(Colorado State Univ. Fort Collins, CO, United States)
Kummerow, Christian D.
(Colorado State Univ. Fort Collins, CO, United States)
Elsaesser, Gregory S.
(Columbia Univ. New York, NY, United States)
Date Acquired
December 6, 2016
Publication Date
November 20, 2016
Publication Information
Publication: International Journal of Remote Sensing
Publisher: Taylor & Francis
Volume: 38
Issue: 1
e-ISSN: 1366-5901
Subject Category
Meteorology And Climatology
Oceanography
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN37699
Funding Number(s)
CONTRACT_GRANT: NA14OAR320125
CONTRACT_GRANT: NNX14AB99A
Distribution Limits
Public
Copyright
Other
Keywords
Climate
TRMM
Variability

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